• Archives of Craniofacial Surgery
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• v.23 no.3
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• pp.111-118
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• 2022

Background: Although the zygomatic arch is an important structure determining facial prominence and width, no consensus exists regarding the classification of isolated zygomatic arch fractures, and the literature on this topic is scarce. To date, five papers have subdivided zygomatic arch fractures; however, only one of those proposed classifications includes the injury vector, although the injury vector is one of the most important factors to consider in fracture cases. Furthermore, the only classification that does include the injury vector is too complicated to be suitable for daily practice. In addition, the existing classifications are clinically limited because they do not consider greenstick fractures, nondisplaced fractures, or coronoid impingement. In the present study, we present a rearrangement of the previously published classifications and propose a modified classification of isolated zygomatic arch fractures that maximizes the advantages and overcomes the disadvantages of previous classification systems. Methods: The classification criteria for isolated zygomatic arch fractures described in five previous studies were analyzed, rearranged, and supplemented to generate a modified classification. The medical records, radiographs, and facial bone computed tomography findings of 134 patients with isolated zygomatic arch fractures who visited our hospital between January 2010 and December 2019 were also retrospectively analyzed. Results: We analyzed major classification criteria (displacement, the force vector of the injury, V-shaped fracture, and coronoid impingement) for isolated zygomatic arch fracture from the five previous studies and developed a modified classification by subdividing zygomatic arch fractures. We applied the modified classification to cases of isolated zygomatic arch fracture at our hospital. The surgery rate and injury severity differed significantly from fracture types I to VI. Conclusion: Using our modified classification, we could determine that both the injury force and the injury vector meaningfully influenced the surgery rate and the severity of the injuries.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.2
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• pp.269-288
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• 2024

To structure the KOS registry, it is necessary to select a classification system that suits the characteristics of the collected KOS. This study aimed to classify domestic KOS collected through various classification schems, and based on these results, provide insights for selecting a classification system when structuring the KOS registry. A total of 313 KOS data collected via web searches were categorized using five types of classification systems and a thesaurus, and the results were analyzed. The analysis indicated that for international linkage of the KOS registry, foreign classification systems should be applied, and for optimization with domestic knowledge resources or to cater to domestic researchers, domestic classification systems need to be applied. Additionally, depending on the field-specific characteristics of the KOS, research area KOS should apply classification systems based on academic disciplines, while public sector KOS should consider classification systems based on government functions. Lastly, it is necessary to strengthen the linkage between domestic and international KOS, which also requires the application of multiple classification systems.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.3
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• pp.65-89
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• 2024

This study developed a model to classify news articles into categories of topic, genre, and region using a Korean Pre-trained Language model. To achieve this, a new news article classification system was designed by referring to the classification systems of domestic media outlets. The topic and genre classification models were implemented as hierarchical classification models that link the main categories and subcategories, and their performance was compared with that of an integrated category model. The evaluation results showed that the hierarchical structure classification model had the advantage of providing more precise categorization in ambiguous or overlapping categories compared to the integrated category model. For regional classification of news articles, a model was built to classify into 18 categories, and for regional news articles, the regional characteristics were clearly reflected in the text, resulting in high performance. This study demonstrated the effectiveness of classifying news articles from multiple perspectives-topic, genre, and region-and emphasized the significance of suggesting the potential for a multi-dimensional news article classification service that meets user needs.

• Korean Journal of Remote Sensing
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• v.28 no.5
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• pp.489-499
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• 2012

In this paper, a classifier ensemble framework for remote sensing data classification is presented that combines classification results generated from both different training sets and different classifiers. A core part of the presented framework is to increase a diversity between classification results by using both different training sets and classifiers to improve classification accuracy. First, different training sets that have different sampling densities are generated and used as inputs for supervised classification using different classifiers that show different discrimination capabilities. Then several preliminary classification results are combined via a majority voting scheme to generate a final classification result. A case study of land-cover classification using multi-temporal ENVISAT ASAR data sets is carried out to illustrate the potential of the presented classification framework. In the case study, nine classification results were combined that were generated by using three different training sets and three different classifiers including maximum likelihood classifier, multi-layer perceptron classifier, and support vector machine. The case study results showed that complementary information on the discrimination of land-cover classes of interest would be extracted within the proposed framework and the best classification accuracy was obtained. When comparing different combinations, to combine any classification results where the diversity of the classifiers is not great didn't show an improvement of classification accuracy. Thus, it is recommended to ensure the greater diversity between classifiers in the design of multiple classifier systems.

• Journal of Service Research and Studies
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• v.8 no.1
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• pp.89-102
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• 2018

The Industrial Classification is a systematic taxonomy of industrial activities and the Standard Industrial Classification is used in all country by their own a consistent classification method. Therefore, it is employed to analyze current status of industry affairs using statistical investigations in terms industrial activities for making industrial policies and to compare industrial activity among countries. Since the Second Industrial Revolution, the need for the homogenous standard of industrial classification among countries emerged as the economic and industrial exchanges between the countries have became more active. In 1940, Colin Clark who british economist divided the industry into the first (primitive), second (processed), and third (service) industries. Based on this, the United Nations Office for Statistics (UNSD) established International Standard Industry Classification (ISIC) in 1948, which most countries invoke it. ISIC(International Standard Industry Classification) and the standard industry classifications of countries have reached the present after several revisions since the enactment of the Act. In the 2000s, the standard industry classification is amended to reflect the emergence of new industries and changes in industrial structure, mainly featuring the creation and segmentation of sections in the tertiary industry domains. It also shows that primary and secondary sectors are shifting to tertiary industry. In this study, the causes of these common phenomena are systematically identified and the problems present classification systems have been analyzed. Also proposed is the direction of formation of the industrial classification system from a service economy point of view and the conceptual model of the new classification system. In the future, it is necessary to validate the proposed model through this study and to carry out various new classification system studies.

• Journal of Korean Library and Information Science Society
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• v.53 no.4
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• pp.123-142
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• 2022

In 2000, North Korea developed and published a two-volume, <Classification-Search Term Dictionary> and is currently used throughout North Korea. The purpose of this study is to examine the development process of the classification schemes of the North Korea after liberation and to understand the contents, composition, and principles of the <Classification-Search Term Dictionary> published in 2000 and revised in 2014. Until now, all the studies of the North Korean classification schemes were studies on the <Book Classification Scheme> published in North Korea in 1964, and there has been no discussion on North Korea's classification schemes since then. The first volume of the <Classification-Search Term Dictionary> consists of 'classification symbols - search terms', and the second volume consists of 'search terms - classification symbols'. Volume 1 is based on the <Books and Bibliography Classification Scheme (1996)>, and there are a total of 41 main classes in five categories. Volume 1 allocates 1 main class (11/19) to 'revolutionary ideas and theories', 8 main classes (20~27) to 'natural sciences', 19 main classes (30~69) to 'engineering technology and applied sciences', 12 main classes (70~85) to 'social sciences', and 1 main class (90) to 'total sciences'. Volume 2 is similar to subject-headings. North Korea's <Classification-Search Term Dictionary> is the first classification scheme introduced in South Korea and is expected to be the starting point for future studies on the establishment of the standard unification classification schemes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4D
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• pp.569-577
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• 2008

The purpose of this study is focused on the development of compound classification process by mixing multitemporal data and annexing a specific image enhancement technique with a specific image classification algorithm, to gain more accurate land information from satellite imagery. That is, this study suggests the classification process using canonical correlation classification technique after principal component analysis for the mixed multitemporal data. The result of this proposed classification process is compared with the canonical correlation classification result of one date images, multitemporal imagery and a mixed image after principal component analysis for one date images. The satellite images which are used are the Landsat 5 TM images acquired on July 26, 1994 and September 1, 1996. Ground truth data for accuracy assessment is obtained from topographic map and aerial photograph, and all of the study area is used for accuracy assessment. The proposed compound classification process showed superior efficiency to appling canonical correlation classification technique for only one date image in classification accuracy by 8.2%. Especially, it was valid in classifying mixed urban area correctly. Conclusively, to improve the classification accuracy when extracting land cover information using Landsat TM image, appling canonical correlation classification technique after principal component analysis for multitemporal imagery is very useful.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.122-128
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• 1998

This paper presents compression of image sequences based on the classification of interframe difference image blocks. classification process consists of image activity classification and energy distribution classification. In the activity classification, interframe difference image blocks are classified into activity blocks and non-activity blocks using the edge detection. In the distribution classification, activity blocks are further classified into vertical blocks, horizontal blocks, and small activity blocks using the AC energy distribution features. The RBFN, trained with numerical classification results, successfully classifies difference image blocks according to image details. Image sequence compressing based on the classification of interframe difference image blocks using the RBFN shows better compression results and less training time than the classical sorting method and the MLP network.

• Journal of the Korean Society for Library and Information Science
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• v.44 no.2
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• pp.241-262
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• 2010

This study aims to perform an evaluation of classification systems provided by major Korean search portals, Naver, Nate, Daum, and Yahoo-Korea. These classification systems are evaluated in terms of the consistency of classification system, logicality of classification system, ease of interface, clarity of category names, order of category and site listing, and hierarchical structure. The results of this study show that each search portal provides separate classification systems for their services. These results imply that it is crucial for search portals to implement a common classification system and a common interface for their services. This study could contribute to the development and improvement of portals' classification systems.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1402-1408
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• 2017

Recently, services for personal biometric data analysis based on real-time monitoring systems has been increasing and many of them have focused on recognition of emotions. In this paper, we propose a classification model to classify anxiety emotion using biometric data actually collected from people. We propose to deploy the support vector machine to build a classification model. In order to improve the classification accuracy, we propose two data pre-processing procedures, which are normalization and data deletion. The proposed algorithms are actually implemented based on Real-time Traffic Flow Measurement structure, which consists of data collection module, data preprocessing module, and creating classification model module. Our experiment results show that the proposed classification model can infers anxiety emotions of people with the accuracy of 65.18%. Moreover, the proposed model with the proposed pre-processing techniques shows the improved accuracy, which is 78.77%. Therefore, we can conclude that the proposed classification model based on the pre-processing process can improve the classification accuracy with lower computation complexity.